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1. From 2024/25/02
I. SUBJECT DESCRIPTION
II. SUBJECT REQUIREMENTS
III. COURSE CURRICULUM
SUBJECT DATA
OBJECTIVES AND LEARNING OUTCOMES
TESTING AND ASSESSMENT OF LEARNING PERFORMANCE
THEMATIC UNITS AND FURTHER DETAILS
Subject name
ESG workshop - Artificial intelligence for ESG compliance
ID (subject code)
BMEGT42RRR5021-00
Type of subject
contact unit
Course types and lessons
Type
Lessons
Lecture
0
Practice
9
Laboratory
0
Type of assessment
obtaining signature
Number of credits
3
Subject Coordinator
Name
Dr. Buzási Attila
Position
associate professor
Contact details
buzasi.attila@gtk.bme.hu
Educational organisational unit for the subject
Department of Environmental Economics and Sustainability
Subject website
https://edu.gtk.bme.hu
Language of the subject
magyar - HU
Curricular role of the subject, recommended number of terms

Programme: „ESG consultant

Subject Role: Compulsory elective

Recommended semester: 2

Direct prerequisites
Strong
None
Weak
None
Parallel
None
Exclusion
None
Validity of the Subject Description
Approved by the Faculty Board of Faculty of Economic and Social Sciences, Decree No: 580387/26/2025 registration number. Valid from: 2025.05.28.

Objectives

The aim of the course is to introduce various aspects of using artificial intelligence through a thematic workshop.

Academic results

Knowledge
  1. Knows the basics of ESG compliance and the role of AI in ESG
  2. Knows the types of data that can be managed with AI
  3. Knows the applications of AI in ESG compliance
  4. Knows the ethical and legal issues of using AI
Skills
  1. Able to identify AI opportunities in a given ESG compliance process
  2. Able to identify automation opportunities in ESG reporting processes
  3. Able to understand basic AI models
Attitude
  1. Uses AI responsibly
  2. Able to consciously manage risk through AI errors and biases
  3. Applies systems thinking, understands that the use of AI can also provide competitive advantage in ESG
Independence and responsibility
  1. Able to independently identify areas where AI should be used for ESG compliance
  2. Able to independently interpret the risks of AI use
  3. Responsible for the implementation of all related management tasks.

Teaching methodology

Practice – full-day workshop

Materials supporting learning

  • Elméleti bevezető diasorok (esettanulmányok, ajánlott irodalom)
  • Ppt slideshow about principles (case studies, optional readings)

General Rules

The learning objectives detailed in 2.2 will be assessed by means of active participation in the workshop.

Performance assessment methods

Checking active participation in the workshop by means of an attendance sheet.

Percentage of performance assessments, conducted during the study period, within the rating

Percentage of exam elements within the rating

Conditions for obtaining a signature, validity of the signature

Active participation in the workshop. Signing the attendance sheet

Issuing grades

%
Excellent 0-100
Very good 0
Good 0
Satisfactory 0
Pass 0
Fail 0

Retake and late completion

As the condition for obtaining a signature is active participation in the workshop, repeat, retake, and late completion are not possible.

Coursework required for the completion of the subject

Nature of work Number of sessions per term
Workshop részvétel 9
Processing background materials 20
Learning individually 20
Záróvizsgára való felkészülés 41

Approval and validity of subject requirements

Consulted with the Faculty Student Representative Committee, approved by the Vice Dean for Education, valid from: 05.05.2024.

Topics covered during the term

Subject includes the topics detailed in the course syllabus to ensure learning outcomes listed under 2.2. can be achieved.

Lecture topics
1. Artificial intelligence for ESG compliance – practical workshop

Additional lecturers

Name Position Contact details

Approval and validity of subject requirements